TY - JOUR
T1 - Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks
AU - Paluszewski, Martin
AU - Hamelryck, Thomas Wim
PY - 2010/3/12
Y1 - 2010/3/12
N2 - Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations).Results: The program package is freely available under the GNU General Public Licence (GPL) from SourceForge http://sourceforge.net/projects/mocapy. The package contains the source for building the Mocapy++ library, several usage examples and the user manual.Conclusions: Mocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail.
AB - Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations).Results: The program package is freely available under the GNU General Public Licence (GPL) from SourceForge http://sourceforge.net/projects/mocapy. The package contains the source for building the Mocapy++ library, several usage examples and the user manual.Conclusions: Mocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail.
U2 - 10.1186/1471-2105-11-126
DO - 10.1186/1471-2105-11-126
M3 - Journal article
C2 - 20226024
SN - 1471-2105
VL - 11
JO - B M C Bioinformatics
JF - B M C Bioinformatics
IS - Suppl 1
M1 - 126
ER -